A NEW HYBRID PROGNOSTIC METHODOLOGY
Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications, this is very rarely the case, and hence the chosen method’s performance can...
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| Format: | Article |
| Language: | English |
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The Prognostics and Health Management Society
2019-06-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: |
| _version_ | 1797812093773152256 |
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| author | Omer F. Eker Fatih Camci Ian K. Jennions |
| author_facet | Omer F. Eker Fatih Camci Ian K. Jennions |
| author_sort | Omer F. Eker |
| collection | DOAJ |
| description | Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications, this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason, a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer-term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available. |
| first_indexed | 2024-03-13T07:33:23Z |
| format | Article |
| id | doaj.art-6919f496c2a34e178380ca6e86e5320e |
| institution | Directory Open Access Journal |
| issn | 2153-2648 |
| language | English |
| last_indexed | 2024-03-13T07:33:23Z |
| publishDate | 2019-06-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj.art-6919f496c2a34e178380ca6e86e5320e2023-06-04T02:31:25ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482019-06-01102https://doi.org/10.36001/ijphm.2019.v10i2.2727A NEW HYBRID PROGNOSTIC METHODOLOGYOmer F. Eker0Fatih Camci1Ian K. Jennions2Artesis, Gebze, Kocaeli, TurkeyAmazon, Austin TX USAIntegrated Vehicle Health Management Centre, Bedfordshire, UKMethodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications, this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason, a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer-term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available.empirical modelphysical modelinghybrid algorithmssimilarity-based modelling |
| spellingShingle | Omer F. Eker Fatih Camci Ian K. Jennions A NEW HYBRID PROGNOSTIC METHODOLOGY International Journal of Prognostics and Health Management empirical model physical modeling hybrid algorithms similarity-based modelling |
| title | A NEW HYBRID PROGNOSTIC METHODOLOGY |
| title_full | A NEW HYBRID PROGNOSTIC METHODOLOGY |
| title_fullStr | A NEW HYBRID PROGNOSTIC METHODOLOGY |
| title_full_unstemmed | A NEW HYBRID PROGNOSTIC METHODOLOGY |
| title_short | A NEW HYBRID PROGNOSTIC METHODOLOGY |
| title_sort | new hybrid prognostic methodology |
| topic | empirical model physical modeling hybrid algorithms similarity-based modelling |
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